Finance Working Papers, Department of Business Studies, Aarhus School of Business, University of Aarhus
No 01-3:
Bootstrap Inference in Semiparametric Generalized Additive Models.
Wolfgang Härdle, Sylvie Huet, Enno Mammen ()
and Stefan Sperlich
Abstract: Semiparametric generalized additive models are a powerful
tool in quantitative econometrics. With response Y , covariates X, T the
model is E(Y | X; T) = G { X T β + α + m1(T1) + . . . + md(Td) }.
Here, G is a known link, â, á are unknown parameters, and m1, . . . , md
are unknown (smooth) functions of possibly higher dimensional covariates
T1, . . . , Td. Estimates of m1, . . . , md, α and β are
presented and asymptotic distribution theory for both the non-parametric
and the parametric part is given. The main focus is the application of
boot-strap methods. It is shown that bootstrap can be used for bias
correction, hypothesis testing (e.g. component-wise analysis) and the
construction of uniform confidence bands. Various bootstrap tests for model
specification and parametrization are given, in particular for testing
additivity and link function specification. The practical performance of
our methods is illustrated in simulations and in an application to
East-West German migration.
43 pages, March 12, 2001
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